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Peter F. Lovibond

Researcher at University of New South Wales

Publications -  113
Citations -  16507

Peter F. Lovibond is an academic researcher from University of New South Wales. The author has contributed to research in topics: Anxiety & Classical conditioning. The author has an hindex of 41, co-authored 104 publications receiving 13794 citations. Previous affiliations of Peter F. Lovibond include University of Sydney & University of Cambridge.

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The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories.

TL;DR: The factor structure of the combined BDI and BAI items was virtually identical to that reported by Beck for a sample of diagnosed depressed and anxious patients, supporting the view that these clinical states are more severe expressions of the same states that may be discerned in normals.
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The propositional nature of human associative learning.

TL;DR: It is argued that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.
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The role of awareness in Pavlovian conditioning: Empirical evidence and theoretical implications

TL;DR: The bulk of the evidence is consistent with the position that awareness is necessary but not sufficient for conditioned performance, although studies suggestive of conditioning without awareness are identified as worthy of further investigation.
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Percentile Norms and Accompanying Interval Estimates from an Australian General Adult Population Sample for Self-Report Mood Scales (BAI, BDI, CRSD, CES-D, DASS, DASS-21, STAI-X, STAI-Y, SRDS, and SRAS)

TL;DR: This article provided percentile norms for a series of self-report mood scales, including self-reported mood scales with very limited normative data, and used them to improve the quality of the data.
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Long-term stability of depression, anxiety, and stress syndromes.

TL;DR: For example, this paper found that each Time 2 scale was best predicted by the same scale at Time 1, with no significant increase in prediction from the other 2 Time 1 scales.